The exemplary embodiment relates to natural language processing and finds particular application in connection with a system and method for assisting an agent to compose a reply to an electronic mail message.
With the advent of on-line support systems, agents are often given the task of replying to email queries. In the customer care case, agents frequently have to reply to similar queries from different customers. These similar queries often solicit replies that are also similar by sharing similar topic structures and vocabulary.
In customer care centers, in order to help the agents send replies to similar queries, the agents often have access to a repository of standard responses. When the agent is preparing a tailored response to a query from the customer, the agent searches among the appropriate standard responses, makes appropriate modifications to the text, fills in information and then sends the reply. This process is both inflexible and time consuming, especially in cases where the customer query is slightly different from one of the expected queries.
Much of the recent work addressing email text analytics has focused on classification and summarization. Email classification has proven useful for spam detection and filtering high-priority messages. Summarization has been useful in processing emails and posts from email groups and discussion forums (Muresan, et al., “Combining linguistic and machine learning techniques for email summarization,” Proc. 2001 Workshop on Computational Natural Language Learning, Vol. 7, Article 19, ACL (2001); and Rambow, et al., “Summarizing email threads,” Proc. HLT-NAACL 2004: Short Papers, pp. 105-108, ACL (2004)). Semi-supervised classification techniques have been proposed for question answering in the context of email threads (Scheffer, “Email answering assistance by semi-supervised text classification,” Intelligent Data Analysis, 8(5):481-493 (2004)). Classification and summarization techniques have been suggested that are based on “speech acts” or “dialog acts” such as proposing a meeting, requesting information (Searle, “A classification of illocutionary acts,” Language in Society, 5(01):1-23 (1976)). Studies involving summarizing of email threads or classification of emails involve dialog-act based analysis have been made (Oya, et al. “Extractive summarization and dialogue act modeling on email threads: An integrated probabilistic approach,” SIGDIAL (2014); Cohen, et al., “Learning to classify email into speech acts,” EMNLP, pp. 309-316 (2004)).
In the domain of customer care, studies have been made for identification of emotional emails related to customer dissatisfaction/frustration and learning possible patterns/phrases for textual re-use in email responses (Gupta, et al., “Emotion detection in email customer care,” Computational Intelligence, 29(3):489-505 (2013); Lamontagne, et al., “Textual reuse for email response,” Adv. in Case-Based Reasoning, pp. 242-256 (2004)). Methods have been proposed for suggesting templates for email responses in the customer care domain (Weng, et al., “Using text classification and multiple concepts to answer e-mails,” Expert Systems with Applications, 26(4):529-543 (2004)).
The problem of discovering the latent structure of topics in spoken and written conversations has been addressed using HMM-based methods handling dialog state transitions and topic content simultaneously (Zhai, et al., “Discovering latent structure in task-oriented dialogues,” ACL (1), pp. 36-46 (2014). However, the method is suited to short alternating conversational utterances, rather than large single email responses typical in customer care field. Further, the method does not predict which topics will likely be employed in a forthcoming agent's response.
There remains a need for a more flexible system and method for proposing replies to an agent for responding to a customer query.